Simulating and evaluating the local behavior of small pedestrian groups

Social aspects of collision avoidance: a detailed analysis of two-person groups and individual pedestrians

Pedestrian groups are commonly found in crowds but research on their social aspects is comparatively lacking. To fill that void in literature, we study the dynamics of collision avoidance between pedestrian groups (in particular dyads) and individual pedestrians in an ecological environment, focusing in particular on (i) how such avoidance depends on the group's social relation (e.g. colleagues, couples, friends or families) and (ii) its intensity of social interaction (indicated by conversation, gaze exchange, gestures etc). By analyzing relative collision avoidance in the "center of mass" frame, we were able to quantify how much groups and individuals avoid each other with respect to the aforementioned properties of the group. A mathematical representation using a potential energy function is proposed to model avoidance and it is shown to provide a fair approximation to the empirical observations. We also studied the probability that the individuals disrupt the group by "passing through it" (termed as intrusion). We analyzed the dependence of the parameters of the avoidance model and of the probability of intrusion on groups' social relation and intensity of interaction. We confirmed that the stronger social bonding or interaction intensity is, the more prominent collision avoidance turns out. We also confirmed that the probability of intrusion is a decreasing function of interaction intensity and strength of social bonding. Our results suggest that such variability should be accounted for in models and crowd management in general. Namely, public spaces with strongly bonded groups (e.g. a family-oriented amusement park) may require a different approach compared to public spaces with loosely bonded groups (e.g. a business-oriented trade fair).

Core Challenges of Social Robot Navigation: A Survey

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in crowded environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in robot planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that could inspire research in the field over the coming decade.

Modeling of crowd groups with an extended social field model

A social field model is extended by adding supports of simulations of crowd group related dynamics such as group cohesion, back-tracking, etc. A computational implementation combining heterogeneous computing and multi-threading technologies is developed to integrate the proposed extension into a heterogeneous computing framework. Hence modeling of group related dynamics can be accomplished in a very efficient manner. In the section of numerical experiments, the extended model is firstly validated with a set of practical data. Results show that the simulated evacuation time matches the practical one quite well. Then a study of fundamental diagrams is expanded with considerations of crowd groups. It is shown that the impact of crowd groups mainly happens in the regime of low densities and would become insignificant in the regime of high densities.

SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigation

The human-robot interaction (HRI) community has developed many methods for robots to navigate safely and socially alongside humans. However, experimental procedures to evaluate these works are usually constructed on a per-method basis. Such disparate evaluations make it difficult to compare the performance of such methods across the literature. To bridge this gap, we introduce SocNavBench , a simulation framework for evaluating social navigation algorithms. SocNavBench comprises a simulator with photo-realistic capabilities and curated social navigation scenarios grounded in real-world pedestrian data. We also provide an implementation of a suite of metrics to quantify the performance of navigation algorithms on these scenarios. Altogether, SocNavBench provides a test framework for evaluating disparate social navigation methods in a consistent and interpretable manner. To illustrate its use, we demonstrate testing three existing social navigation methods and a baseline method on SocNavBench , showing how the suite of metrics helps infer their performance trade-offs. Our code is open-source, allowing the addition of new scenarios and metrics by the community to help evolve SocNavBench to reflect advancements in our understanding of social navigation.

A history of crowd simulation: the past, evolution, and new perspectives

This paper aims to discuss the past, evolution, and new perspectives in crowd simulation. Many work have been produced and published in this area that was launched approximately 30 years ago. In this paper, we re-visited the main aspects of the area, presenting the periods and evolution we had in the past. In addition, we also discuss the present and possible trends for the future.

Group-walking effect on bidirectional pedestrian flow in a corridor

Groups are commonly found in general crowds and their behaviors are distinguished from that of isolated pedestrians. Thus, in recent five years researchers have started to investigate pedestrian group movement. In this paper, we considered group walking effect and introduced group floor field to the traditional floor field model. Furthermore, two different methods of generating group floor field were put forward, i.e. group center generation (method 1 for short) and group leader generation (method 2 for short), and we applied the proposed group model to simulate bidirectional pedestrian flow in a corridor. No matter which method of generating group floor field is adopted, the simulation results show that group members walk slower than singles, and with the group size increasing the transition point from the free flow phase to the jamming has a decrease trend. In addition, it seems that method 2 of generating group floor field makes group more cohesive and stable at the same crowd density than method 1. Afterwards it is found that the crowd with large group size is more easily affected by asymmetric injection rate. At last, people’s walking preference is shortly discussed, and it is obtained that people’s walking preference is also good for group movement from the perspective of movement efficiency.

Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization

Interactive multi-agent simulation algorithms are used to compute the trajectories and behaviors of different entities in virtual reality scenarios. However, current methods involve considerable parameter tweaking to generate plausible behaviors. We introduce a novel approach (Heter-Sim) that combines physics-based simulation methods with data-driven techniques using an optimization-based formulation. Our approach is general and can simulate heterogeneous agents corresponding to human crowds, traffic, vehicles, or combinations of different agents with varying dynamics. We estimate motion states from real-world datasets that include information about position, velocity, and control direction. Our optimization algorithm considers several constraints, including velocity continuity, collision avoidance, attraction, direction control. Other constraints are implemented by introducing a novel energy function to control the motions of heterogeneous agents. To accelerate the computations, we reduce the search space for both collision avoidance and optimal solution computation. Heter-Sim can simulate tens or hundreds of agents at interactive rates and we compare its accuracy with real-world datasets and prior algorithms. We also perform user studies that evaluate the plausible behaviors generated by our algorithm and a user study that evaluates the plausibility of our algorithm via VR.

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.

Fuzzy Evaluation of Crowd Safety Based on Pedestrians' Number and Distribution Entropy

Crowd video monitoring and analysis is a hot topic in computer vision and public management. The pre-evaluation of crowd safety is beneficial to the prediction of crowd status to avoid the occurrence of catastrophic events. This paper proposes a method to evaluate crowd safety based on fuzzy inference. Pedestrian's number and distribution uniformity are considered in a fuzzy inference system as two kinds of attributes of a crowd. Firstly, the pedestrian's number is estimated by the number of foreground pixels. Then, the distribution uniformity of a crowd is calculated using distribution entropy by dividing the monitoring scene into several small areas. Furthermore, through the fuzzy operation, the fuzzy system is constructed by using two input variables (pedestrian's number and distribution entropy) and one output variable (crowd safety status). Finally, inference rules between the crowd safety state and the pedestrian's number and distribution uniformity are constructed to obtain the pre-evaluation of the safety state of the crowd. Three video sequences extracted from different scenes are used in the experiment. Experimental results show that the proposed method can be used to evaluate the safety status of the crowd in a monitoring scene.

Human motion trajectory prediction: a survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Realistic Multi-Agent Formation Using Discretionary Group Behavior (DGB)

Simulating groups and their behaviors have been one of the important topics recently. This paper proposes a novel velocity-based method to simulate the realistic behavior of groups moving in a specific formation in a virtual environment including other groups and obstacles. The proposed algorithm, we called “DGB—Discretionary Group Behavior”, takes advantage of ORCA (Optimal Reciprocal Collision Avoidance) half-planes for both grouping and collision avoidance strategy. By considering new half-planes for each agent, we can have more reasonable and intelligent behavior in front of challenging obstacles and other agents. Unlike recent similar works, independent members in a group do not have predefined connections to each other even though they can keep the group’s formation while moving and trying to follow their best neighbors discretionarily in critical situations. Through experiments, we found that the proposed algorithm can yield more human-like group behavior in a crowd of agents.

Energy Level-Based Abnormal Crowd Behavior Detection

The change of crowd energy is a fundamental measurement for describing a crowd behavior. In this paper, we present a crowd abnormal detection method based on the change of energy-level distribution. The method can not only reduce the camera perspective effect, but also detect crowd abnormal behavior in time. Pixels in the image are treated as particles, and the optical flow method is adopted to extract the velocities of particles. The qualities of different particles are distributed as different value according to the distance between the particle and the camera to reduce the camera perspective effect. Then a crowd motion segmentation method based on flow field texture representation is utilized to extract the motion foreground, and a linear interpolation calculation is applied to pedestrian’s foreground area to determine their distance to the camera. This contributes to the calculation of the particle qualities in different locations. Finally, the crowd behavior is analyzed according to the change of the consistency, entropy and contrast of the three descriptors for co-occurrence matrix. By calculating a threshold, the timestamp when the crowd abnormal happens is determined. In this paper, multiple sets of videos from three different scenes in UMN dataset are employed in the experiment. The results show that the proposed method is effective in characterizing anomalies in videos.

Going Through, Going Around: A Study on Individual Avoidance of Groups

When avoiding a group, a walker has two possibilities: either he goes through it or around it. Going through very dense groups or around huge ones would not seem natural and could break any sense of presence in a virtual environment. This paper aims to enable crowd simulators to handle such situations correctly. To this end, we need to understand how real humans decide to go through or around groups. As a first hypothesis, we apply the Principle of Minimum Energy (PME) on different group sizes and density. According to this principle, a walker should go around small and dense groups whereas he should go through large and sparse groups. Such principle has already been used for crowd simulation; the novelty here is to apply it to decide on a global avoidance strategy instead of local adaptations only. Our study quantifies decision thresholds. However, PME leaves some inconclusive situations for which the two solutions paths have similar energetic costs. In a second part, we propose an experiment to corroborate PME decisions thresholds with real observations. As controlling the factors of an experiment with many people is extremely hard, we propose to use Virtual Reality as a new method to observe human behavior. This work represents the first crowd simulation algorithm component directly designed from a VR-based study. We also consider the role of secondary factors in inconclusive situations. We show the influence of the group appearance and direction of relative motion in the decision process. Finally, we draw some guidelines to integrate our conclusions to existing crowd simulators and show an example of such integration. We evaluate the achieved improvements.

Interactive formation control in complex environments

The degrees of freedom of a crowd is much higher than that provided by a standard user input device. Typically, crowd-control systems require multiple passes to design crowd movements by specifying waypoints, and then defining character trajectories and crowd formation. Such multi-pass control would spoil the responsiveness and excitement of real-time control systems. In this paper, we propose a single-pass algorithm to control a crowd in complex environments. We observe that low-level details in crowd movement are related to interactions between characters and the environment, such as diverging/merging at cross points, or climbing over obstacles. Therefore, we simplify the problem by representing the crowd with a deformable mesh, and allow the user, via multitouch input, to specify high-level movements and formations that are important for context delivery. To help prevent congestion, our system dynamically reassigns characters in the formation by employing a mass transport solver to minimize their overall movement. The solver uses a cost function to evaluate the impact from the environment, including obstacles and areas affecting movement speed. Experimental results show realistic crowd movement created with minimal high-level user inputs. Our algorithm is particularly useful for real-time applications including strategy games and interactive animation creation.